But, while this might not be an indication of an error, it sure is a reason to worry. Because if each new alignment researcher pursues some new pathway, and can be sped up a little but not a ton by research-partners and operational support, then no matter how many new alignment visionaries we find, we aren’t much decreasing the amount of time it takes to find a solution.
I’m not really convinced by this! I think a way to model this would be to imagine the “key” researchers as directed MCMC agents exploring the possible solution space. Maybe something like HMC, their intuition is modelled by the fact that they have a hamiltonian to guide them instead of being random-walk MCMC. Even then, having multiple agents would allow for the relevant minima to be explored much more quickly.
Taking this analogy further, there is a maximum number of agents beyond which you won’t be mapping out the space more quickly. This is because chains need some minimum length for burn-in, to discard correlated samples, etc. In the research world, I think it just means people take a while to get their ideas somewhere useful, and subsequent work tends to be evolutionary instead of revolutionary over short time-scales; only over long time-scales does work seem revolutionary. The question then is this: are we in the sparse few-agents regime, or in the crowded many-agents regime? This isn’t my field, but if I were to hazard a guess as an outsider, I’d say it sure feels like the former. In the latter, I’d imagine most people, even extremely productive researchers, would routinely find their ideas to have already been looked at before. It feels like that in my field, but I don’t think I am a visionary my ideas are likely more “random-walk” than “hamiltonian”.
I’m not really convinced by this! I think a way to model this would be to imagine the “key” researchers as directed MCMC agents exploring the possible solution space. Maybe something like HMC, their intuition is modelled by the fact that they have a hamiltonian to guide them instead of being random-walk MCMC. Even then, having multiple agents would allow for the relevant minima to be explored much more quickly.
Taking this analogy further, there is a maximum number of agents beyond which you won’t be mapping out the space more quickly. This is because chains need some minimum length for burn-in, to discard correlated samples, etc. In the research world, I think it just means people take a while to get their ideas somewhere useful, and subsequent work tends to be evolutionary instead of revolutionary over short time-scales; only over long time-scales does work seem revolutionary. The question then is this: are we in the sparse few-agents regime, or in the crowded many-agents regime? This isn’t my field, but if I were to hazard a guess as an outsider, I’d say it sure feels like the former. In the latter, I’d imagine most people, even extremely productive researchers, would routinely find their ideas to have already been looked at before. It feels like that in my field, but I don’t think I am a visionary my ideas are likely more “random-walk” than “hamiltonian”.